System and method for arranging concept clusters in thematic relationships in a two-dimensional visual display area

ABSTRACT

A system and method for arranging concept clusters in thematic relationship in a two-dimensional visual display space is described. A plurality of stored clusters are selected from a multi-dimensional visualization space sharing a common theme including at least one concept. Each theme logically represents one or more concepts based on terms extracted from a document set. Each cluster not yet grouped from the selected clusters for the shared common theme is combined in order into a list of placeable clusters. Each clusters list is grafted into a grouping of one or more other clusters lists at an anchor point. The anchor point includes an open edge formed along a vector defined from the center of one such cluster in the grouping. The clusters in each other clusters list share at least one concept represented in the shared common theme.

FIELD OF THE INVENTION

The present invention relates in general to data visualization and, inparticular, to a system and method for arranging concept clusters inthematic relationships in a two-dimensional visual display space.

BACKGROUND OF THE INVENTION

Computer-based data visualization involves the generation andpresentation of idealized data on a physical output device, such as acathode ray tube (CRT), liquid crystal diode (LCD) display, printer andthe like. Computer systems visualize data through graphical userinterfaces (GUIs) which allow intuitive user interaction and highquality presentation of synthesized information.

The importance of effective data visualization has grown in step withadvances in computational resources. Faster processors and larger memorysizes have enabled the application of complex visualization techniquesto operate in multi-dimensional concept space. As well, theinterconnectivity provided by networks, including intranetworks andinternetworks, such as the Internet, enable the communication of largevolumes of information to a wide-ranging audience. Effective datavisualization techniques are needed to interpret information and modelcontent interpretation.

The use of a visualization language can enhance the effectiveness ofdata visualization by communicating words, images and shapes as asingle, integrated unit. Visualization languages help bridge the gapbetween the natural perception of a physical environment and theartificial modeling of information within the constraints of a computersystem. As raw information cannot always be digested as written words,data visualization attempts to complement and, in some instances,supplant the written word for a more intuitive visual presentationdrawing on natural cognitive skills.

Effective data visualization is constrained by the physical limits ofcomputer display systems. Two-dimensional and three-dimensionalinformation can be readily displayed. However, n-dimensional informationin excess of three dimensions must be artificially compressed. Carefuluse of color, shape and temporal attributes can simulate multipledimensions, but comprehension and usability become difficult asadditional layers of modeling are artificially grafted into the finitebounds of display capabilities.

Thus, mapping multi-dimensional information into a two- orthree-dimensional space presents a problem. Physical displays arepractically limited to three dimensions. Compressing multi-dimensionalinformation into three dimensions can mislead, for instance, the viewerthrough an erroneous interpretation of spatial relationships betweenindividual display objects. Other factors further complicate theinterpretation and perception of visualized data, based on the Gestaltprinciples of proximity, similarity, closed region, connectedness, goodcontinuation, and closure, such as described in R. E. Horn, “VisualLanguage: Global Communication for the 21^(st) Century,” Ch. 3, MacroVUPress (1998), the disclosure of which is incorporated by reference.

In particular, the misperception of visualized data can cause amisinterpretation of, for instance, dependent variables as independentand independent variables as dependent. This type of problem occurs, forexample, when visualizing clustered data, which presents discretegroupings of data, which are misperceived as being overlaid oroverlapping due to the spatial limitations of a three-dimensional space.

Consider, for example, a group of clusters, each cluster visualized inthe form of a circle defining a center and a fixed radius. Each clusteris located some distance from a common origin along a vector measured ata fixed angle from a common axis through the common origin. The radiiand distances are independent variables relative to the other clustersand the radius is an independent variable relative to the common origin.In this example, each cluster represents a grouping of pointscorresponding to objects sharing a common set of traits. The radius ofthe cluster reflects the relative number of objects contained in thegrouping. Clusters located along the same vector are similar in theme asare those clusters located on vectors having a small cosine rotationfrom each other. Thus, the angle relative to a common axis' distancefrom a common origin is an independent variable with a correlationbetween the distance and angle reflecting relative similarity of theme.Each radius is an independent variable representative of volume. Whendisplayed, the overlaying or overlapping of clusters could mislead theviewer into perceiving data dependencies where there are none.

Therefore, there is a need for an approach to presenting arbitrarilydimensioned data in a finite-dimensioned display space while preservingindependent data relationships. Preferably, such an approach wouldorganize the data according to theme and place thematically-relatedclusters into linear spatial arrangements to maximize the number ofrelationships depicted.

There is a further need for an approach to selecting and orienting dataclusters to properly visualize independent and dependent variables whilecompressing thematic relationships for display.

SUMMARY OF THE INVENTION

The present invention provides a system and method for organizing andplacing groupings of thematically-related clusters in a visual displayspace. Each cluster size equals the number of concepts (relateddocuments) contained in the cluster. Clusters sharing a common theme areidentified. Individual lists of thematically-related clusters are sortedand categorized into sublists of placeable clusters. Anchor pointswithin each sublist are identified. Each anchor point has at least oneopen edge at which to graft other thematically-related cluster sublists.Cluster sublists are combined at the anchor points to form groupings,which are placed into the visual display space. The mostthematically-relevant cluster grouping is placed at the center of thevisual display space.

An embodiment provides a system and method for generating atwo-dimensional spatial arrangement of a multi-dimensional clusterrendering. A set of clusters is selected from a concept space. Thecluster space includes a multiplicity of clusters visualizing documentcontent based on extracted terms. Each cluster in the clusters setshares a common theme including shared terms. An anchor point on atleast one such cluster within the clusters set is determined. The anchorpoint includes at least one open edge formed along a vector defined fromthe center of the at least one such cluster. The clusters in theclusters set are arranged into an arrangement of adjacent clustersoriginating from the anchor point at one such open edge.

A further embodiment provides a system and method for arranging conceptclusters in thematic relationship in a two-dimensional visual displayspace. A plurality of stored clusters are selected from amulti-dimensional visualization space sharing a common theme includingat least one concept. Each theme logically represents one or moreconcepts based on terms extracted from a document set. Each cluster notyet grouped from the selected clusters for the shared common theme iscombined in order into a list of placeable clusters. Each clusters listis grafted into a grouping of one or more other clusters lists at ananchor point. The anchor point includes an open edge formed along avector defined from the center of one such cluster in the grouping. Theclusters in each other clusters list share at least one conceptrepresented in the shared common theme.

Still other embodiments of the present invention will become readilyapparent to those skilled in the art from the following detaileddescription, wherein is described embodiments of the invention by way ofillustrating the best mode contemplated for carrying out the invention.As will be realized, the invention is capable of other and differentembodiments and its several details are capable of modifications invarious obvious respects, all without departing from the spirit and thescope of the present invention. Accordingly, the drawings and detaileddescription are to be regarded as illustrative in nature and not asrestrictive.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram showing a system for arranging conceptclusters in thematic relationships in a two-dimensional visual displayspace, in accordance with the present invention.

FIG. 2 is a graph showing, by way of example, a corpus graph of thefrequency of concept occurrences generated by the system of FIG. 1.

FIG. 3 is a data representation diagram showing, by way of example, aview of a cluster spine generated by the cluster display system of FIG.1.

FIGS. 4(A)-(C) are data representation diagrams showing anchor pointswithin cluster spines.

FIG. 5 is a data representation diagram showing, by way of example, aview of a thematically-related cluster spine grafted onto the clusterspine of FIG. 3.

FIG. 6 is a data representation diagram showing, by way of example, aview of singleton clusters and further thematically-related clusterspines grafted onto the cluster spine of FIG. 5.

FIG. 7 is a data representation diagram showing, by way of example, aview of a cluster spine of non-circular clusters generated by thecluster display system of FIG. 1.

FIG. 8 is a data representation diagram showing, by way of example, aview of a thematically-related cluster spine grafted onto an end-pointcluster of the cluster spine of FIG. 3.

FIG. 9 is a flow diagram showing a method for arranging concept clustersin thematic relationships in a two-dimensional visual display space, inaccordance with the present invention.

FIG. 10 is a routine for sizing clusters for use in the method of FIG.8.

FIG. 11 is a routine for building sublists of placeable clusters for usein the method of FIG. 8.

FIGS. 12(A)-(B) are a routine for placing clusters for use in the methodof FIG. 8.

FIG. 13 is a graph showing, by way of example, an anchor point within acluster spine generated by the cluster display system of FIG. 1.

FIG. 14 is a routine for placing groupers for use in the method of FIG.8.

DETAILED DESCRIPTION

FIG. 1 is a block diagram 10 showing a system for arranging conceptclusters in thematic relationships in a two-dimensional visual displayspace, in accordance with the present invention. The system consists ofa cluster display system 11, such as implemented on a general-purposeprogrammed digital computer. The cluster display system 11 is coupled toinput devices, including a keyboard 12 and a pointing device 13, such asa mouse, and display 14, including a CRT, LCD display, and the like. Aswell, a printer (not shown) could function as an alternate displaydevice. The cluster display system 11 includes a processor, memory andpersistent storage, such as provided by a storage device 16, withinwhich are stored clusters 17 representing visualized multi-dimensionaldata. The cluster display system 11 can be interconnected to othercomputer systems, including clients and servers, over a network 15, suchas an intranetwork or internetwork, including the Internet, or variouscombinations and topologies thereof, as would be recognized by oneskilled in the art.

Each cluster 17 represents a grouping of one or more points in avirtualized concept space, as further described below beginning withreference to FIG. 3. Preferably, the clusters 17 are stored asstructured data sorted into an ordered list in ascending or descendingorder. In the described embodiment, each cluster represents individualconcepts and themes extracted from a set of documents 21 and categorizedbased on, for example, Euclidean distances calculated between each pairof concepts and themes and defined within a pre-specified range ofvariance, such as described in common-assigned U.S. Pat. No. 6,888,548,issued May 3, 2005, the disclosure of which is incorporated byreference.

The cluster display system 11 includes three modules: classifier 18,placement 19, and display and visualize 20. The classifier module 18sorts a list of clusters 17 into either ascending or descending orderbased cluster sizes. The placement module 19 selects and orients thesized clusters to properly visualize independent and dependent variableswhile compressing thematic relationships for visual display. Theplacement module 19 logically includes a list building submodule forcreating sublists of placeable dusters 17, a cluster placement submodulefor placing clusters 17 into displayable groupings, known as “groupers,”and a grouper placement submodule for placing the groupers within avisual display area. Finally, the display and visualize module 20performs the actual display of the clusters 17 via the display 14responsive to commands from the input devices, including keyboard 12 andpointing device 13.

The individual computer systems, including cluster display system 11,are general purpose, programmed digital computing devices consisting ofa central processing unit (CPU), random access memory (RAM),non-volatile secondary storage, such as a hard drive or CD ROM drive,network interfaces, and peripheral devices, including user interfacingmeans, such as a keyboard and display. Program code, including softwareprograms, and data are loaded into the RAM for execution and processingby the CPU and results are generated for display, output, transmittal,or storage.

Each module is a computer program, procedure or module written as sourcecode in a conventional programming language, such as the C++ programminglanguage, and is presented for execution by the CPU as object or bytecode, as is known in the art. The various implementations of the sourcecode and object and byte codes can be held on a computer-readablestorage medium or embodied on a transmission medium in a carrier wave.The cluster display system 11 operates in accordance with a sequence ofprocess steps, as further described below with reference to FIG. 8.

FIG. 2 is a graph showing, by way of example, a corpus graph 30 of thefrequency of concept occurrences generated by the system of FIG. 1. Thecorpus graph 30 visualizes concepts extracted from a collection ofdocuments 21 (shown in FIG. 1) represented by weighted clusters ofconcepts, such as described in commonly-assigned U.S. Pat. No.6,978,274, issued Dec. 20, 2005, pending, the disclosure of which isincorporated by reference. The x-axis 31 defines the individual conceptsfor all documents 21 and the y-axis 32 defines the number of documents21 referencing each concept. The individual concepts are mapped in orderof descending frequency of occurrence 33 to generate a curve 34representing the latent semantics of the documents set.

A median value 35 is selected and edge conditions 36 a-b are establishedto discriminate between concepts which occur too frequently versusconcepts which occur too infrequently. Those documents 21 falling withinthe edge conditions 36 a-b form a subset of documents 21 containinglatent concepts. In the described embodiment, the median value 35 isdocument-type dependent. For efficiency, the upper edge condition 36 bis set to 70% and the 64 concepts immediately preceding the upper edgecondition 36 b are selected, although other forms of thresholddiscrimination could also be used.

FIG. 3 is a data representation diagram 40 showing, by way of example, aview 41 of a cluster spine 42 generated by the cluster display system ofFIG. 1. Each cluster in the cluster spine 42, such as endpoint clusters44 and 46 and midpoint clusters 45, group documents 21 sharing the samethemes and falling within the edge conditions 36 a-b of the corpus graph40 (shown in FIG. 2).

In the described embodiment, cluster size equals the number of conceptscontained in the cluster. The cluster spine 42 is built by identifyingthose clusters 44-46 sharing a common theme. A theme combines two ormore concepts 47, which each group terms or phrases (not shown) withcommon semantic meanings. Terms and phrases are dynamically extractedfrom a document collection through latent concept evaluation. Duringcluster spine creation, those clusters 44-46 having available anchorpoints within each cluster spine 42 are identified for use in graftingother cluster spines sharing thematically-related concepts, as furtherdescribed below with reference to FIG. 5.

The cluster spine 42 is placed into a visual display area to generate atwo-dimensional spatial arrangement. To represent datainter-relatedness, the clusters 44-46 in each cluster spine 42 areplaced along a vector 43 arranged in decreasing cluster size, althoughother line shapes and cluster orderings can be used.

FIGS. 4(A)-(C) are data representation diagrams 50, 60, 65 respectivelyshowing anchor points within cluster spines 51, 61, 66. A cluster havingat least one open edge constitutes an anchor point. Referring first toFIG. 4(A), a largest endpoint cluster 52 of a cluster spine 51 functionsas an anchor point along each open edge 55 a-e. The endpoint cluster 52contains the largest number of concepts.

An open edge is a point along the edge of a cluster at which anothercluster can be adjacently placed. Slight overlap within 20% with otherclusters is allowed. An open edge is formed by projecting vectors 54 a-eoutward from the center 53 of the endpoint cluster 52, preferably atnormalized angles. The clusters in the cluster spine 51 are arranged inorder of decreasing cluster size.

In the described embodiment, the normalized angles for largest endpointclusters are at approximately ±60° to minimize interference with otherspines while maximizing the degree of interrelatedness between spines.Five open edges 55 a-e are available to graft other thematically-relatedcluster spines. Other evenly divisible angles could be also used. Asfurther described below with reference to FIG. 5, otherthematically-related cluster spines can be grafted to the endpointcluster 52 at each open edge 55 a-e.

Referring next to FIG. 4(B). a smallest endpoint cluster 62 of a clusterspine 61 also functions as an anchor point along each open edge. Theendpoint cluster 62 contains the fewest number of concepts. The clustersin the cluster spine 61 are arranged in order of decreasing clustersize. An open edge is formed by projecting vectors 64 a-c outward fromthe center 63 of the endpoint cluster 62, preferably at normalizedangles.

In the described embodiment, the normalized angles for smallest endpointclusters are at approximately ±60°, but only three open edges areavailable to graft other thematically-related cluster spines.Empirically, limiting the number of available open edges to those facingthe direction of decreasing cluster size helps to maximize theinterrelatedness of the overall display space.

Referring finally to FIG. 4(C), a midpoint cluster 67 of a cluster spine61 functions as an anchor point for a cluster spine 66 along each openedge. The midpoint cluster 67 is located intermediate to the clusters inthe cluster spine 66 and defines an anchor point along each open edge.An open edge is formed by projecting vectors 69 a-b outward from thecenter 68 of the midpoint cluster 67, preferably at normalized angles.Unlike endpoint clusters 52, 62 the midpoint cluster 67 can only serveas an anchor point along tangential vectors non-coincident to the vectorforming the cluster spine 66. Accordingly, endpoint clusters 52, 62include one additional open edge serving as a coincident anchor point.

In the described embodiment, the normalized angles for midpoint clustersare at approximately ±60°, but only two open edges are available tograft other thematically-related cluster spines. Empirically, limitingthe number of available open edges to those facing the direction ofdecreasing cluster size helps to maximize the interrelatedness of theoverall display space.

FIG. 5 is a data representation diagram 70 showing, by way of example, aview 71 of a thematically-related cluster spine 72 grafted onto thecluster spine 42 of FIG. 3. Each cluster in the cluster spine 72,including endpoint cluster 74 and midpoint clusters 75, share conceptsin common with the midpoint cluster 76 of the cluster spine 42.Accordingly, the cluster spine 72 is “grafted” onto the cluster spine 42at an open edge of an available anchor point on midpoint cluster 76. Thecombined grafted clusters form a cluster grouping or “grouper” ofclusters sharing related or similar themes.

FIG. 6 is a data representation diagram 80 showing, by way of example, aview 81 of singleton clusters 86 and further thematically-relatedcluster spines 82 and 84 grafted onto the cluster spine 42 of FIG. 3.The clusters in the cluster spines 82 and 84 share concepts in commonwith the clusters of cluster spine 42 and are grafted onto the clusterspine 82 at open edges of available anchor points. Slight overlap 87between grafted clusters is allowed. In the described embodiment, nomore than 20% of a cluster can be covered by overlap. The singletonclusters 86, however, do not thematically relate to the clusters incluster spines 42, 72, 82, 84 and are therefore grouped as individualclusters in non-relational placements.

FIG. 7 is a data representation diagram 100 showing, by way of example,a view 101 of a cluster spine 102 of non-circular clusters 104-106generated by the cluster display system of FIG. 1. Each cluster in thecluster spine 102, including endpoint clusters 104, 106 and midpointclusters 105, has a center of mass c_(m) 107 a-e and is oriented along acommon vector 103.

As described above, with reference to FIG. 3, each cluster 104-106represents multi-dimensional data modeled in a two-dimensional visualdisplay space. Each cluster 104-106 is non-circular and defines a convexvolume representing data located within the multi-dimensional conceptspace. The center of mass c_(m) 107 a-e for each cluster 104-106 islogically located within the convex volume and is used to determine openedges at each anchor point. A segment is measured from the center ofmass c_(m) 107 a-e for each cluster 104-106. An open edge is formed atthe intersection of the segment and the edge of the non-circularcluster. By way of example, the clusters 104-106 represent non-circularshapes that are convex and respectively comprise a circle, a square, anoctagon, a triangle, and an oval, although other forms of convex shapescould also be used, either singly or in combination therewith, as wouldbe recognized by one skilled in the art.

FIG. 8 is a data representation diagram 110 showing, by way of example,a view 111 of a thematically-related cluster spine grafted onto anend-point cluster of the cluster spine of FIG. 3.

Further thematically-related cluster spines 112, 114, 116, 118 aregrafted into the cluster spine 62. The cluster spines 112, 114, 118 aregrafted into the largest endpoint cluster of the cluster spine 62 withthe cluster spine 112 oriented along a forward-facing axis 113 and thecluster spine 114 oriented along a backward-facing axis 115. The clusterspine 116 is grafted onto a midpoint cluster of the cluster spine 114along a backward-facing axis 117. Note the cluster spine 116 has overlap119 with a cluster in the cluster spine 114.

FIG. 9 is a flow diagram 120 showing a method for arranging conceptclusters in thematic relationships in a two-dimensional visual displayspace in accordance with the present invention. The method presentsarbitrarily dimensioned concept data visualized in a two-dimensionalvisual display space in a manner that preserves independent datarelationships between clusters.

First, individual clusters are sized by number of concepts (relateddocuments) contained in each cluster (block 121), as further describedbelow with reference to FIG. 10. The sized clusters are then analyzed tofind shared terms (block 122). In the described embodiment, thoseclusters sharing one or more semantically relevant concepts areconsidered thematically-related.

The lists of shared terms are then sorted into sublists of clustersbased on the number of clusters that share each term (block 123). Thesublists are arranged in order of decreasing cluster size. Next, listsof placeable clusters are built (block 124), as further described belowwith reference to FIG. 10. Each list contains those clusters sharing acommon theme and which had not yet been placed in the visual displayspace. The clusters in each sublist are placed into individual groupingsor “groupers” to form cluster spines (block 125), as further describedbelow with reference to FIG. 11. The method then terminates.

FIG. 10 is a routine for sizing clusters 130 for use in the method ofFIG. 8. The purpose of this routine is to determine the size of eachcluster based on the number of concepts contained in the cluster.

Each cluster is iteratively sized in a processing loop (blocks 131-133)as follows. For each cluster processed in the processing loop (block131), the cluster size is set to equal the number of concepts containedin the cluster (block 132). Iterative processing continues (block 133)for each remaining cluster. The groupers are then placed into the visualdisplay space (block 134), as further described below with reference toFIG. 13. Finally, the placed groupers are displayed (block 135), afterwhich the routine terminates.

FIG. 11 is a flow diagram showing a routine for building sublists ofplaceable clusters 140 for use in the method of FIG. 9. The purpose ofthis routine is to build sublists of thematically-related clusters toform individual cluster spines. The cluster spines are the buildingblocks used to form cluster groupings or “groupers.”

The sublists are built by iteratively processing each shared concept inan outer processing loop (blocks 141-150) as follows. For each newshared concept processed in the outer processing loop (block 141), asublist of clusters belonging to the shared concept is built (block142). A cluster center represents a seed value originating from theshared concept. A seed value typically consists of the core set ofconcepts, preferably including one or more concepts, which form thebasis of the current sublist. Thereafter, each of the clusters isiteratively processed in an inner processing loop (blocks 143-149) todetermine sublist membership, as follows.

For each cluster processed in the inner processing loop (block 143), ifthe cluster does not belong to the current sublist (block 144), that is,the cluster does not share the common concept, the cluster is skipped(block 149). Otherwise, if the cluster has not already been placed inanother sublist (block 145), the cluster is added to the current sublist(block 146). Otherwise, if the cluster has been placed (block 145) andhas an open edge (block 147), the cluster is marked as a anchor point(block 148). Iterative processing of each cluster (block 149) and sharedconcept (block 150) continues, after which the routine returns.

FIGS. 12(A)-(B) are a routine for placing clusters 160 for use in themethod of FIG. 9. The purpose of this routine is to form clustergroupings or “groupers” of grafted cluster spines.

Each sublist of placeable clusters is iteratively processed in an outerprocessing loop (blocks 161-175), as follows. For each sublist processedin the outer processing loop (block 161), if the sublist includes ananchor point (block 162), the anchor point is selected (block 165).Otherwise, a new grouper is started (block 163) and the first cluster inthe sublist is selected as the anchor point and removed from the sublist(block 164). Each cluster in the sublist is then iteratively processedin an inner processing loop (blocks 166-173), as follows.

For each cluster processed in the inner processing loop (block 166), theradius of the cluster is determined (block 167) and the routine attemptsto place the cluster along the open vectors emanating from the anchorpoint (block 168). The radius is needed to ensure that the placedclusters do not overlap. If the cluster was not successfully placed(block 169), the cluster is skipped and processed during a furtheriteration (block 175). Otherwise, if the cluster is successfully placed(block 169) and is also designated as an anchor point (block 170), theangle of the anchor point is set (block 171), as further described belowwith reference to FIGS. 12(A)-(B). The cluster is then placed in thevector (block 172). Processing continues with the next cluster (block173).

Upon the completion of the processing of each cluster in the sublist(block 166), the angle for the cluster is set if the cluster is selectedas an anchor point for a grafted cluster (block 174). Processingcontinues with the next sublist (block 175), after which the routinereturns.

FIG. 13 is a graph 180 showing, by way of example, an anchor pointwithin a cluster spine generated by the cluster display system ofFIG. 1. Anchor points 186, 187 are formed along an open edge at theintersection of a vector 183 a, 183 b, respectively, drawn from thecenter 182 of the cluster 181. The vectors are preferably drawn at anormalized angle, such as 60° in the described embodiment, relative tothe vector 188 forming the cluster spine.

A cluster 181 functioning as an anchor point can have one or more openedges depending upon the placement of adjacent clusters and upon whetherthe cluster 181 is the largest endpoint, smallest endpoint or, as shown,midpoint cluster. In the described embodiment, largest endpoint clustershave four open edges, smallest endpoint clusters have three open edges,and midpoint clusters have two open edges. Adjusting the normalizedangle and allowing more (or less) overlap between grafted cluster spinesare possible to allow for denser (or sparser) cluster placements.

FIG. 14 is a routine for placing groupers 190 in the use of the methodof FIG. 9. The purpose of this routine is to place groupings of clustersublists into a visual display space.

Each of the groupers is iteratively processed in a processing loop(blocks 191-197), as follows. For each grouper processed in theprocessing loop (block 191), if the grouper comprises a singletoncluster (block 192), the grouper is skipped (block 197). Otherwise, ifthe grouper is the first grouper selected (block 193), the grouper iscentered at the origin of the visual display space (block 194).Otherwise, the angle of the grouper and radius from the center of thedisplay are incremented by the size of the grouper, plus extra space toaccount for the radius of the end-point cluster at which the cluster isgrafted (block 195) until the grouper can be placed withoutsubstantially overlapping any previously-placed grouper. Slight overlapwithin 20° between clusters is allowed. A grouper is added to thedisplay space (block 196). Iterative processing continues with the nextgrouper (block 197). Finally, all singleton groupers are placed in thedisplay space (block 198). In the described embodiment, the singletongroupers are placed arbitrarily in the upper left-hand corner, althoughother placements of singleton groupers are possible, as would berecognized by one skilled in the art. The routine then returns.

Although the foregoing method 120 of FIG. 9 has been described withreference to circular clusters, one skilled in the art would recognizethat the operations can be equally applied to non-circular clustersforming closed convex volumes.

While the invention has been particularly shown and described asreferenced to the embodiments thereof, those skilled in the art willunderstand that the foregoing and other changes in form and detail maybe made therein without departing from the spirit and scope of theinvention.

1. A system for generating a two-dimensional spatial arrangement of acluster rendering, comprising: clusters stored in a storage to representconcepts and terms extracted from a set of documents; a set of thestored clusters selected with each selected cluster sharing a commontheme comprising one or more of the extracted concepts and terms thatare shared; and a placement module to place the set of the storedclusters into a grouping, comprising: an anchor point selector submoduleto choose one of the selected clusters and to determine an anchor pointon the chosen cluster that is located on an open edge of the chosencluster along a vector defined from a center of the chosen cluster,wherein the vector intersects the anchor point; and a cluster placementsubmodule to place a center of a further selected cluster outside of theanchor point on the vector and to limit overlap of the chosen clusterand the further selected cluster; and an arrangement submodule toarrange one or more of the remaining selected clusters into anarrangement of clusters that each have a center originating outside ofthe anchor point and on the vector; and a display and visualizationmodule to display the grouping via a display.
 2. A system according toclaim 1, further comprising: a sort module to sort the selected clustersin the set of the stored clusters by cluster size.
 3. A system accordingto claim 1, further comprising: an angle submodule to define the vectorat a normalized angle.
 4. A system according to claim 1, furthercomprising: a rendering module to render each selected cluster as acircle having an independent radius.
 5. A system according to claim 4,wherein each circle has a volume dependent on a number of conceptscontained in the selected cluster.
 6. A system according to claim 1,further comprising: a rendering module to render each selected clusteras a convex volume, wherein each convex shape represents visualized datafor a semantic concept space.
 7. A system according to claim 1, whereinthe placement module determines a further anchor point located onanother open edge of the chosen cluster where a center of a furtherselected cluster is placed outside the further anchor point on a furthervector and limits overlap of the chosen cluster and the further selectedcluster, further comprising: a grafting submodule arranging one or moreof the remaining selected clusters into an additional arrangement ofclusters that each have a center originating outside of the furtheranchor point and on the further vector.
 8. A system according to claim1, wherein the common theme is defined by selecting the shared extractedterms to have common semantic meanings.
 9. A system according to claim1, wherein at least one additional set of the stored clusters areselected with each selected additional cluster sharing a further commontheme comprising one or more of the extracted terms that are shared,wherein the further common theme is different than the common theme; andthe at least one additional set of the stored clusters is placed intothe grouping.
 10. A system according to claim 1, wherein at least oneadditional cluster is selected comprising the extracted terms that areunique from each other cluster; and the at least one additional clusteris placed into the grouping.
 11. A method for generating atwo-dimensional spatial arrangement of a cluster rendering, comprising:storing clusters in a storage to represent concepts and terms extractedfrom a set of documents; selecting a set of the clusters with eachselected cluster sharing a common theme comprising one or more of theextracted concepts and terms that are shared; and placing the set of thestored clusters into a grouping, comprising: choosing one of theselected clusters and determining an anchor point on the chosen clusterthat is located on an open edge of the chosen cluster along a vectordefined from a center of the chosen cluster, wherein the vectorintersects the anchor point; and placing a center of a further selectedcluster outside of the anchor point on the vector and limiting overlapof the chosen cluster and the further selected cluster; and arrangingone or more of the remaining selected clusters into an arrangement ofclusters that each have a center originating outside of the anchor pointand on the vector; and displaying the grouping via a display.
 12. Amethod according to claim 11, further comprising: sorting the selectedclusters in the set of the stored clusters by cluster size.
 13. A methodaccording to claim 11, further comprising: defining the vector at anormalized angle.
 14. A method according to claim 11, furthercomprising: rendering each selected cluster as a circle having anindependent radius.
 15. A method according to claim 14, furthercomprising: calculating a volume for each circle dependent on a numberof concepts contained in the selected cluster.
 16. A method according toclaim 11, further comprising: rendering each cluster as a convex volume,wherein each convex shape represents visualized data for a semanticconcept space.
 17. A method according to claim 11, further comprising:determining a further anchor point located on another open edge of thechosen cluster where a center of a further selected cluster is placedoutside the further anchor point on a further vector and limitingoverlap of the chosen cluster and the further selected cluster; andarranging one or more of the remaining selected clusters into anadditional arrangement of clusters that each have a center originatingoutside of the further anchor point and on the further vector.
 18. Acomputer-readable storage medium storing code for causing a computer toperform the method according to claim
 11. 19. A method according toclaim 11, further comprising: defining the common theme by selecting theshared extracted terms to have common semantic meanings.
 20. A methodaccording to claim 11, further comprising: selecting at least oneadditional set of the stored clusters with each selected additionalcluster sharing a further common theme comprising one or more of theextracted terms that are shared, wherein the further common theme isdifferent than the common theme; and placing the at least one additionalset of the stored clusters into the grouping.
 21. A method according toclaim 11, further comprising: selecting at least one additional clustercomprising the extracted terms that are unique from each other cluster;and placing the at least one additional cluster into the grouping.
 22. Asystem for arranging concept clusters in thematic relationship in atwo-dimensional visual display area, comprising: a stored theme tologically represent one or more concepts based on terms extracted from adocument set; a plurality of clusters selected to represent amulti-dimensional visualization space stored as clusters in a storage,wherein each selected cluster comprises at least one of the concepts inone such theme that is in common with the other selected clusters; and aplacement module to place the clusters into a grouping, comprising: alisting submodule to combine in order each ungrouped cluster from theselected clusters for the shared common theme into a list of placeableclusters; a grouping submodule to add each placeable clusters list intothe grouping with one or more other placeable clusters lists, whereinthe clusters in the other placeable clusters lists each comprise atleast one concept in the shared common theme; an anchor submodule tochoose a selected cluster and to determine an anchor point on the chosencluster that is located on an open edge of the chosen cluster along avector defined from a center of the chosen cluster, wherein the vectorintersects the anchor point; and a cluster placement submodule to placea center of a further selected cluster outside of the anchor point onthe vector and to limit overlap of the chosen cluster and the furtherselected cluster; and a grafting submodule to place the center of aselected cluster and to graft the clusters in the remaining placeableclusters lists in the grouping outside the anchor point and along thevector; and a display and visualization module to display the clustersvia a display.
 23. A system according to claim 22, further comprising: asort module sorting the clusters in each placeable clusters list insequence.
 24. A system according to claim 23, wherein the sequencecomprises a number of documents containing the one or more logicallyrepresented concepts.
 25. A system according to claim 23, wherein thesequence comprises one of ascending and descending order.
 26. A systemaccording to claim 22, wherein each cluster is formed as one of acircular and non-circular convex volume.
 27. A system according to claim22, wherein the vector is defined at normalized angles.
 28. A systemaccording to claim 22, wherein the shared common theme contains conceptswithin a pre-specified range of variance.
 29. A system according toclaim 22, wherein at least one additional plurality of the clusters isselected, wherein each selected additional cluster comprises one or moreof the extracted terms that is in common with the other selectedclusters in a further common theme that is different than the sharedcommon theme; and the at least one additional plurality of the clustersis placed into the grouping.
 30. A system according to claim 22, whereinat least one additional cluster is selected that comprises the extractedterms that are unique from each other cluster; and the at least oneadditional cluster is placed into the grouping.
 31. A method forarranging concept clusters in thematic relationship in a two-dimensionalvisual display area, comprising: logically representing one or moreconcepts based on terms extracted from a document set as a theme;selecting clusters representing a multi-dimensional visualization spacestored as clusters in a storage, wherein each selected cluster comprisesat least one of the concepts in one such theme that is in common withthe other selected clusters; and placing the clusters into a grouping,comprising: combining in order each ungrouped cluster from the selectedclusters for the shared common theme into a list of placeable clusters;adding each placeable clusters list into the grouping with one or moreother placeable clusters lists, wherein the clusters in the otherplaceable clusters lists each comprise at least one concept in theshared common theme; choosing a selected cluster and determining ananchor point on the chosen cluster that is located on an open edge ofthe chosen cluster along a vector defined from a center of the chosencluster, wherein the vector intersects the anchor point; and placing acenter of a further selected cluster outside of the anchor point on thevector and limiting overlap of the chosen cluster and the furtherselected cluster; and placing the center of a selected cluster andgrafting the clusters in the remaining placeable clusters lists in thegrouping outside the anchor point along the vector; and displaying thegrouping via a display.
 32. A method according to claim 31, furthercomprising: sorting the clusters in each placeable clusters list insequence.
 33. A method according to claim 32, wherein the sequencecomprises a number of documents containing the one or more logicallyrepresented concepts.
 34. A method according to claim 32, wherein thesequence comprises one of ascending and descending order.
 35. A methodaccording to claim 31, further comprising: forming each cluster as oneof a circular and non-circular convex volume.
 36. A method according toclaim 31, further comprising: defining the vector at normalized angles.37. A method according to claim 31, wherein the shared common themecontains concepts within a pre-specified range of variance.
 38. Acomputer-readable storage medium storing code for causing a computer toperform the method according to claim
 31. 39. A method according toclaim 31, further comprising: selecting additional clusters, whereineach selected additional cluster comprises one or more of the extractedterms that is in common with the other selected clusters in a furthercommon theme that is different than the shared common theme; and placingthe additional clusters into the grouping.
 40. A method according toclaim 31, further comprising: selecting at least one additional clustercomprising the extracted terms that are unique from each other cluster;and placing the at least one additional cluster into the grouping.